H2O Score

Type

ml-predict

Class

fire.nodes.h2o.NodeH2OScore

Fields

Name	Title	Description
isTestData	is Test Data	To enable the test metrics.
label	Label Column	The label column for model Transformation.
withContribution	Compute Shapley Values	Compute Shapley Values and it supported for Regression and Binomial Classification problem.
path	Path To Save Shapley Contributions	Path to save the Shapley contributions for test dataset.
modelType	Model Type	Select the type of the model.
confusionMatrix	Confusion Matrix
output_confusion_matrix_chart	Output Confusion Matrix Chart	Whether to display Confusion Matrix Chart.
cmChartTitle	Confusion Matrix Chart Title	Title name to display in Confusion Matrix Chart
cmChartDescription	Confusion Matrix Chart Description	Description to display in Confusion Matrix Chart
confusionMatrixTargetLegend	Confusion Matrix Target Legend	Legend name to display for Target in Confusion Matrix
confusionMatrixPredictedLabelLegend	Confusion Matrix PredictedLabel Legend	Legend name to display for Predicted Label in Confusion Matrix
Description	Confusion Matrix Description
confusionMatrixRowDescription	Confusion Matrix Outcome description	Add the business details of the outcome of the confusion matrix rows
ROC Curve	ROC Curve
output_roc_chart	Output ROC Curve	Whether to display confusion matrix chart.
roc_title	ROC Curve Chart Title	Title name to display in ROC Curve Chart
roc_description	ROC Curve Chart Description	Add Description for ROC Curve Chart
xlabel	X Label	X label
ylabel	Y Label	Y Label
predictionOverTime	Prediction Over Time
model_uuid	Model UUID	Enter the model uuid
enablePredictionOverTimeMetrics	Enable Prediction Over Time Metrics	enable
modelCategory	Model Category	Select the category

Details

H2O Score Node

This node scores a new dataset using an existing H2O model. It takes a trained H2O model and an input DataFrame as input and generates predictions.

Examples

H2O Score Node Example

Scenario:

Let’s say you have trained an H2O model to predict customer churn. You can use the H2O Score node to apply this model to a new dataset of customer data and generate churn predictions.

Configuration:

1. H2O Model: Select the trained H2O model to use for scoring from model load node

2. Output Storage Level: Choose the storage level for the output DataFrame.

Output:

The node will output a new DataFrame containing the original input data along with the predicted values.